Abstract
Multi-task learning is a paradigm shown to improve the performance of related tasks through their joint learning. However, for real-world data, it is usually difficult to assess the task relatedness and joint learning with unrelated tasks may lead to serious performance degradations. To this end, we propose a framework that groups the tasks based on their relatedness in a subspace and allows a varying degree of relatedness among tasks by sharing the subspace bases across the groups. This provides the flexibility of no sharing when two sets of tasks are unrelated and partial/total sharing when the tasks are related. Importantly, the number of task-groups and the subspace dimensionality are automatically inferred from the data. To realize our framework, we introduce a novel Bayesian nonparametric prior that extends the traditional hierarchical beta process prior using a Dirichlet process to permit potentially infinite number of child beta processes. We apply our model for multi-task regression and classification applications. Experimental results using several synthetic and real datasets show the superiority of our model to other recent multi-task learning methods.
Original language | English |
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Pages | 1694-1702 |
Number of pages | 9 |
Publication status | Published - 1 Jan 2013 |
Event | 30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States of America Duration: 16 Jun 2013 → 21 Jun 2013 |
Conference
Conference | 30th International Conference on Machine Learning, ICML 2013 |
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Country | United States of America |
City | Atlanta, GA |
Period | 16/06/13 → 21/06/13 |